A New SSOPMV Learning for Matrix Data Sets

In real-world applications, most multi-view data sets are semi-supervised and large-scale. In order to process these data sets, scholars have developed semi-supervise done-pass multi-view learning (SSOPMV). While SSOPMV cannot process matrix data sets. Thus this manuscript extends the model of SSOPM...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2018-12, Vol.466 (1), p.12111
Main Authors: Zhu, Changming, Mei, Chengjiu, Zhou, Rigui, Wei, Lai, Zhang, Xiafen, Yao, Min
Format: Article
Language:English
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Datasets
Machine learning
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Summary:In real-world applications, most multi-view data sets are semi-supervised and large-scale. In order to process these data sets, scholars have developed semi-supervise done-pass multi-view learning (SSOPMV). While SSOPMV cannot process matrix data sets. Thus this manuscript extends the model of SSOPMV to matrix version and the new learning machine is named matrix-instance-based SSOPMV, i.e. (MSSOPMV). Related experiments validate that MSSOPMV can process multi-view, semi-supervised, large-scale, and matrix data sets well.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/466/1/012111